Data analytics for intelligent transportation systems / edited by Mashrur Chowdhury, Amy Apon, Kakan Dey.

Format:

Book

Contributor:

Chowdhury, Mashrur, editor.

Apon, Amy, editor.

Dey, Kakan, editor.

Language:

English

Subjects (All):

Intelligent transportation systems.

Intelligent transportation systems--Data processing.

Physical Description:

1 online resource (404 pages) : illustrations (some color)

Edition:

1st ed.

Place of Publication:

Amsterdam, Netherlands ; Oxford, England ; Cambridge, Massachusetts : Elsevier, 2017.

Summary:

Data Analytics for Intelligent Transportation Systems provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems that includes detailed coverage of the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce.It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Users will learn how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning.- Includes case studies in each chapter that illustrate the application of concepts covered- Presents extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies- Contains contributors from both leading academic and commercial researchers- Explains how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications

Contents:

Front Cover

Data Analytics for Intelligent Transportation Systems

Copyright Page

Dedication

Contents

About the Editors

About the Contributors

Preface

Acknowledgments

1 Characteristics of Intelligent Transportation Systems and Its Relationship With Data Analytics

1.1 Intelligent Transportation Systems as Data-Intensive Applications

1.1.1 ITS Data System

1.1.2 ITS Data Sources and Data Collection Technologies

1.2 Big Data Analytics and Infrastructure to Support ITS

1.3 ITS Architecture: The Framework of ITS Applications

1.3.1 User Services and User Service Requirements

1.3.2 Logical Architecture

1.3.3 Physical Architecture

1.3.4 Service Packages

1.3.5 Standards

1.3.6 Security

1.4 Overview of ITS Applications

1.4.1 Types of ITS Applications

1.4.2 ITS Application and Its Relationship to Data Analytics

1.5 Intelligent Transportation Systems Past, Present, and Future

1.5.1 1960'S and 1970'S

1.5.2 1980'S and 1990'S

1.5.3 2000'S

1.5.4 2010'S and Beyond

1.6 Overview of Book: Data Analytics for ITS Applications

Exercise Problems

References

2 Data Analytics: Fundamentals

2.1 Introduction

2.2 Functional Facets of Data Analytics

2.2.1 Descriptive Analytics

2.2.1.1 Descriptive Statistics

2.2.1.2 Exploratory Data Analysis

2.2.1.3 Exploratory Data Analysis Illustration

2.2.1.4 Exploratory Data Analysis Case Studies

2.2.2 Diagnostic Analytics

2.2.2.1 Diagnostic Analytics Case Studies

2.2.2.1.1 Student Success System

2.2.2.1.2 COPA

2.2.2.1.3 Diagnostic Analytics in Teaching and Learning

2.2.3 Predictive Analytics

2.2.3.1 Predictive Analytics Use Cases

2.2.4 Prescriptive Analytics

2.3 Evolution of Data Analytics

2.3.1 SQL Analytics: RDBMS, OLTP, and OLAP.

2.3.2 Business Analytics: Business Intelligence, Data Warehousing, and Data Mining

2.3.2.1 Business Intelligence

2.3.2.2 Data Warehouses, Star Schema, and OLAP Cubes

2.3.2.3 ETL Tools

2.3.2.4 OLAP Servers

2.3.2.5 Data Mining

2.3.3 Visual Analytics

2.3.4 Big Data Analytics

2.3.5 Cognitive Analytics

2.4 Data Science

2.4.1 Data Lifecycle

2.4.2 Data Quality

2.4.3 Building and Evaluating Models

2.5 Tools and Resources for Data Analytics

2.6 Future Directions

2.7 Chapter Summary and Conclusions

2.8 Questions and Exercise Problems

3 Data Science Tools and Techniques to Support Data Analytics in Transportation Applications

3.1 Introduction

3.2 Introduction to the R Programming Environment for Data Analytics

3.3 Research Data Exchange

3.4 Fundamental Data Types and Structures: Data Frames and List

3.4.1 Data Frame

3.4.2 List

3.5 Importing Data from External Files

3.5.1 Delimited

3.5.2 XML

3.5.3 SQL

3.6 Ingesting Online Social Media Data

3.6.1 Static Search

3.6.2 Dynamic Streaming

3.7 Big Data Processing: Hadoop MapReduce

3.8 Summary

3.9 Exercises

4 The Centrality of Data: Data Lifecycle and Data Pipelines

4.1 Introduction

4.2 Use Cases and Data Variability

4.3 Data and its Lifecycle

4.3.1 The USGS Lifecycle Model

4.3.2 Digital Curation Center (DCC) Curation Model

4.3.3 DataONE Model

4.3.4 SEAD Research Object Lifecycle Model

4.4 Data Pipelines

4.5 Future Directions

4.6 Chapter Summary and Conclusions

4.7 Exercise Problems and Questions

4.7.1 Exercise 1. Defining and Describing Research Data

4.7.2 Exercise 2. Mapping Research Project onto the Lifecycle

4.7.3 Exercise 3. Data Organization

4.7.4 Exercise 4. Data Pipelines

References.

5 Data Infrastructure for Intelligent Transportation Systems

5.1 Introduction

5.2 Connected Transport System Applications and Workload Characteristics

5.3 Infrastructure Overview

5.4 Higher-Level Infrastructure

5.4.1 MapReduce and Beyond: Scalable Data Processing

5.4.2 Data Ingest and Stream Processing

5.4.3 SQL and Dataframes

5.4.4 Short-Running and Random Access Data Management

5.4.5 Search-Based Analytics

5.4.6 Business Intelligence and Data Science

5.4.7 Machine Learning

5.5 Low-Level Infrastructure

5.5.1 Hadoop: Storage and Compute Management

5.5.2 Hadoop in the Cloud

5.6 Chapter Summary and Conclusions

Exercise Problems and Questions

6 Security and Data Privacy of Modern Automobiles

6.1 Introduction

6.2 Connected Vehicle Networks and Vehicular Applications

6.2.1 In-Vehicle Networks

6.2.2 External Networks

6.2.3 Innovative Vehicular Applications

6.3 Stakeholders and Assets

6.4 Attack Taxonomy

6.5 Security Analysis

6.5.1 Network and Protocol Vulnerability Analysis

6.5.2 Attacks

6.5.2.1 Antitheft system attacks

6.5.2.2 ECU attacks

6.5.2.3 TPMS attacks

6.5.2.4 VANETs attacks

6.6 Security and Privacy Solutions

6.6.1 Cryptography Basics

6.6.2 Security Solutions for Bus Communications

6.6.2.1 Code obfuscation

6.6.2.2 Authentication, confidentiality, and integrity

6.6.2.2.1 Authentication

6.6.2.2.2 Confidentiality

6.6.2.2.3 Integrity

6.6.2.3 Rootkit traps

6.6.2.4 Intrusion detection system

6.6.2.5 Gateway firewall

6.6.3 WPAN Security and Privacy

6.6.3.1 Bluetooth security checklist

6.6.3.2 Secure WPAN

6.6.3.3 Enabling data privacy in WPAN

6.6.4 Secure VANETs

6.6.5 Secure OTA ECU Firmware Update

6.6.6 Privacy Measurement of Sensor Data

6.6.7 Secure Handover

6.7 Future Research Directions.

6.8 Summary and Conclusions

6.9 Exercises

7 Interactive Data Visualization

7.1 Introduction

7.2 Data Visualization for Intelligent Transportation Systems

7.3 The Power of Data Visualization

7.4 The Data Visualization Pipeline

7.5 Classifying Data Visualization Systems

7.6 Overview Strategies

7.6.1 Data Quantity Reduction

7.6.2 Miniaturizing Visual Glyphs

7.7 Navigation Strategies

7.7.1 Zoom and Pan

7.7.2 Overview+Detail

7.7.3 Focus+Context

7.8 Visual Interaction Strategies

7.8.1 Selecting

7.8.2 Linking

7.8.3 Filtering

7.8.4 Rearranging and Remapping

7.9 Principles for Designing Effective Data Visualizations

7.10 A Case Study: Designing a Multivariate Visual Analytics Tool

7.10.1 Multivariate Visualization Using Interactive Parallel Coordinates

7.10.2 Dynamic Queries Through Direct Manipulation

7.10.3 Dynamic Variable Summarization via Embedded Visualizations

7.10.4 Multiple Coordinated Views

7.11 Chapter Summary and Conclusions

7.12 Exercises

7.13 Sources for More Information

7.13.1 Journals

7.13.2 Conferences

8 Data Analytics in Systems Engineering for Intelligent Transportation Systems

8.1 Introduction

8.2 Background

8.2.1 Systems Development V Model

8.2.1.1 Project initiation

8.2.1.2 Preliminary engineering

8.2.1.3 Plans, specifications, and estimates

8.2.1.4 Construction

8.2.1.5 Project closeout

8.2.1.6 Operations and maintenance

8.2.2 Continuous Engineering

8.2.3 AADL

8.2.3.1 Language overview

8.2.3.2 Behavior annex

8.2.3.3 Error annex

8.2.3.4 AGREE

8.2.3.5 Resolute

8.3 Development Scenario

8.3.1 Data Analytics in Architecture

8.3.2 The Scenario

8.4 Summary and Conclusion

8.5 Exercises

8.6 Appendix A

8.6.1 EMV2 Error Ontology

9 Data Analytics for Safety Applications

9.1 Introduction

9.2 Overview of Safety Research

9.2.1 Human Factors

9.2.2 Crash Count/Frequency Modeling

9.2.3 Before and After Study

9.2.4 Crash Injury Severity Modeling

9.2.5 Commercial Vehicle Safety

9.2.6 Data Driven Highway Patrol Plan

9.2.7 Deep Learning from Big and Heterogeneous Data for Safety

9.2.8 Real-Time Traffic Operation and Safety Monitoring

9.2.9 Connected Vehicles and Traffic Safety

9.3 Safety Analysis Methods

9.3.1 Statistical Methods

9.3.1.1 Count data modeling

9.3.1.2 Categorical data modeling

9.3.2 Artificial Intelligence and Machine Learning

9.4 Safety Data

9.4.1 Crash Data

9.4.2 Traffic Data

9.4.3 Roadway Data

9.4.4 Weather Data

9.4.5 Vehicle and Driver Data

9.4.6 Naturalistic Driving Study

9.4.7 Big Data and Open Data Initiatives

9.4.8 Other Data

9.5 Issues and Future Directions

9.5.1 Issues With Existing Safety Research

9.5.2 Future Directions

9.6 Chapter Summary and Conclusions

9.7 Exercise Problems and Questions

10 Data Analytics for Intermodal Freight Transportation Applications

10.1 Introduction

10.1.1 ITS-Enabled Intermodal Freight Transportation

10.1.2 Data Analytics for ITS-Enabled Intermodal Freight Transportation

10.2 Descriptive Data Analytics

10.2.1 Univariate Analysis

10.2.1.1 Chi-squared test

10.2.1.2 K-S test

10.2.1.3 A-D test

10.2.1.4 Comments on chi-squared, K-S, and A-D tests

10.2.2 Bivariate Analysis

10.3 Predictive Data Analytics

10.3.1 Bivariate Analysis

10.3.2 Multivariate Analysis

10.3.3 Fuzzy Regression

10.4 Summary and Conclusions

10.5 Exercise Problems

10.6 Solution to Exercise Problems

11 Social Media Data in Transportation

11.1 Introduction to Social Media.

11.2 Social Media Data Characteristics.

Notes:

Includes bibliographical references at the end of each chapters and index.

Description based on print version record.

ISBN:

0-12-809851-1